Substantial progress has been made in the last few years on the development of large planar arrays of detectors for use in three-dimensional positron tomography, the simplest of these designs consisting of a stationary pair of drift-chamber detectors operating in coincidence. Such a system carries significant promise as a low-cost, high resolution imaging device with particular applications in myocardial perfusion studies. However, because of the limited acceptance angle of these cameras, conventional image reconstruction by filtered back-projection is not applicable, and variations on conventional methods have had only limited success. The proposed research will develop and test a generically new 'Monte-Carlo' reconstruction method which seems particularly appropriate to these devices. The method envisaged takes advantage of previously noted similarities between the mathematics of image reconstruction and problems in statistical thermodynamics. It does so by adapting some of the Monte-Carlo (MC) computer simulation techniques which have been found useful in the latter field. Although the introduction of MC methods into image reconstruction is a substantial break with current directions of research in this field, there are encouraging parallels in other fields; MC methods have recently been suggested as a possible general method of handling optimization problems. Thus an investigation of MC in the proposed application should encourage the exploration of these methods for other medical imaging purposes. While our early work has established general principles by which MC can be incorporated into positron tomography, a full software implementation of the method must be done to address the practical questions of its feasibility, e.g. computer time and memory requirements and the character of the reconstructed images. This evaluation will be done using both computer generated data and using data from a drift chamber positron camera currently under development at Brigham and Women's Hospital.